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作 者:伍铁斌 朱红求[2] 龙文[3] 李勇刚[2] 刘云连[1] WU Tiebin;ZHU Hongqiu;LONG Wen;LI Yonggang;LIU Yunlian(College of Energy and Electrical Engineering,Hunan University of Humanities,Science and Technology,Loudi 417000,China;School of Automation,Central South University,Changsha 410083,China;Key Laboratory of Economics System Simulation,Guizhou University of Finance and Economics,Guiyang 550025,China)
机构地区:[1]湖南人文科技学院能源与机电工程学院,湖南娄底417000 [2]中南大学自动化学院,湖南长沙410083 [3]贵州财经大学贵州省经济系统仿真重点实验室,贵州贵阳550025
出 处:《中南大学学报(自然科学版)》2020年第1期103-111,共9页Journal of Central South University:Science and Technology
基 金:国家自然科学基金创新群体研究项目(61621062);国家自然科学基金重大资助项目(61890932);湖南省教育厅重点资助项目(19A254);贵州省高等学校科技拔尖人才支持计划项目(黔教合KY字[2017]070);娄底市科技计划项目(2017,2018)~~
摘 要:针对标准鲸鱼优化算法(WOA)易早熟的缺点,提出一种改进的鲸鱼算法。设计一种基于指数函数的收敛因子以协调WOA算法的探索与开发能力,提出基于旋转操作的精英学习算法来提高算法的局部寻优能力,并采用自适应变异操作以减少算法陷入局部最优的概率。通过10个标准测试函数验证改进的鲸鱼算法IWOA的有效性。研究结果表明:IWOA具有收敛速度快、全局寻优能力强的优点;将IWOA算法应用于钢铁烧结的配料过程,有效降低了配料过程的成本。Aiming at the shortcomings of standard whale optimization algorithm(WOA),an improved whale algorithm was proposed.An updating formula of convergence factor based on exponential function was designed to coordinate the abilities of exploration and exploitation.In order to improve the local searching ability of the algorithm,an optimal learning strategy based on a rotation operation was proposed for elite individual,which strengthened the local searching ability of the algorithm.Adaptive mutation operation was adopted to reduce the probability that the algorithm falled into the local optimum.Simulation experiments were conducted on the 10 test functions.The simulation results show that the improved whale optimation algroithm(IWOA)has better performance in convergence rate and global optimization ability than other comparison methods.The algorithm is applied in the blending process in the sintering process of iron and steel,which effectively reduces the cost of the blending process.
关 键 词:鲸鱼优化算法 指数函数 旋转操作 变异 烧结配料
分 类 号:TP273.1[自动化与计算机技术—检测技术与自动化装置]
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